Comparative Analysis of Pso-Pid and Hu-Pid

Chanda Thakur. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.18-23
www.ijera.com DOI: 10.9790/9622- 0702031823 18 | P a g e
Comparative Analysis of Pso-Pid and Hu-Pid
Chanda Thakur, Navdeep Batish
Power system-M.tech(Scholar) I.K.G Punjab Technical University Associate Prof.-S.S.C.E.T.
ABSTRACT
PID control is an important ingredient of a distributed control system. The controllers are also embedded in
many special purpose control systems. PID control is often combined with logic, sequential functions, selectors,
and simple function blocks to build the complicated automation systems used for energy production,
transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are
also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set
points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter‟ of
power system engineering. It is an important component in every control engineer‟s tool box. PID controllers
have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic
tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence on the PID controller
Keywords: Include at least 5 keywords or phrases
I. INTRODUCTION
The PID controller is the most common
form of feedback. It was an essential element of
early governors and it became the standard tool
when process control emerged in the 1940s. In
process control today, more than 95% of the control
loops are of PID type, most loops are actually PI
control. PID controllers are today found in all areas
where control is used. The controllers come in
many different forms. There are standalone systems
in boxes for one or a few loops, which are
manufactured by thousands yearly. PID control is
an important ingredient of a distributed control
system. The controllers are also embedded in many
special purpose control systems [15]. PID control is
often combined with logic, sequential functions,
selectors, and simple function blocks to build the
complicated automation systems used for energy
production, transportation, and manufacturing.
Many sophisticated control strategies, such as
model predictive control, are also organized
hierarchically. PID control is used at the lowest
level; the multivariable controller gives the set
points to the controllers at the lower level. The PID
controller can thus be said to be the “bread and
butter‟ of control engineering. It is an important
component in every control engineer‟s tool box.
PID controllers have survived many changes in
technology, from mechanics and pneumatics to
microprocessors via electronic tubes, transistors,
integrated circuits. The microprocessor has had a
dramatic influence on the PID controller [16]. In
general it can be said that P controller cannot
stabilize higher order processes. For the 1st order
processes, meaning the processes with one energy
storage, a large increase in gain can be tolerated.
Proportional controller can stabilize only 1st order
unstable process. Changing controller gain K can
change closed loop dynamics. A large controller
gain will result in control system with: Smaller
steady state error, i.e. better reference following,
faster dynamics, i.e. broader signal frequency band
of the closed loop system and larger sensitivity with
respect to measuring noise and smaller amplitude
and phase margin. When P controller is used,
large gain is needed to improve steady state
error. Stable systems do not have problems when
large gain is used. Such systems are systems with
one energy storage (1st order capacitive systems).
If constant steady state error can be accepted with
such processes, than P controller can be used. Small
steady state errors can be accepted if sensor will
give measured value with error or if
importance of measured value is not too great
anyway.
D mode is used when prediction of the
error can improve control or when it necessary
to stabilize the system. From the frequency
characteristic of D element it can be seen that it
has phase lead of 90°. Often derivative is not
taken from the error signal but from the
system output variable. This is done to avoid
effects of the sudden change of the reference input
that will cause sudden change in the value of error
signal. Sudden change in error signal will cause
sudden change in control output. To avoid that it is
suitable to design D mode to be proportional to the
change of the output variable. PD controller is
often used in control of moving objects such
are flying and underwater vehicles, ships, rockets
etc. One of the reason is in stabilizing effect of
PD controller on sudden changes in heading
variable y(t). Often a "rate gyro" for velocity
measurement is used as sensor of heading change
RESEARCH ARTICLE OPEN ACCESS

of moving object. PI controller will eliminate
forced oscillations and steady state error resulting
in operation of on-off controller and P controller
respectively. However, introducing integral mode
has a negative effect on speed of the response
and overall stability of the system. Thus, PI
controller will not increase the speed of response.
It can be expected since PI controller does not have
means to predict what will happen with the error in
near future. This problem can be solved by
introducing derivative mode which has ability to
predict what will happen with the error in near
future and thus to decrease a reaction time of the
controller. PI controllers are very often used in
industry, especially when speed of the response
is not an issue. A control without D mode is used
when: Fast response of the system is not required ,
large disturbances and noise are present during
operation of the process ,there is only one energy
storage in process (capacitive or inductive) ,there
are large transport delays in the system.
Process control has become increasingly
important in the process industries as a
consequence of global competition, rapidly
changing economic conditions, and more stringent
environmental and safety regulations. Process
control is also a critical concern in the development
of more flexible and more flexible and more
complex processes for manufacturing high value
added products. Any study of process control must
begin by investigating the concept of a process. It is
generally thought of as a place where materials and
most often, energy come together to produce a
desired product. From a control viewpoint the
meaning is more specific. A process is identified as
leaving one or more variables associated with it that
are important enough for their values to be known
and for them to be controlled. One of the complex
and difficult in process control ids control tuning.
Control tuning is the major key issue to operate the
plant. Process tuning is a key role in ensuring that
the plant performance satisfies the operating
objectives. Controller tuning inevitably involves a
tradeoff between performance and robustness. The
performance goals of excellent set-point tracking
and disturbance rejection should be balanced
against the robustness goal of stable operation over
a wide range of conditions.
The Process control system is the entity
that is charged with the responsibility for
monitoring outputs, making decisions about how
best to manipulate inputs so as to obtain desired
output behavior, and effectively implement such
decisions on the process [3]. Control systems are
classified into two general categories open loop and
closed loop control systems. Open loop control
systems are control systems in which the output has
no effect upon the control action. In an open-loop
control system, the output is neither measured nor
fed back for comparison with the input. For
example, in a washing machine, soaking, washing,
and rinsing are operated on a time basis. The
machine does not measure the output signal namely
the cleanliness of clothes. In any open-loop control
system the output is not compared with the
reference input. Hence, for each reference input,
there corresponds a fixed operation condition. Thus,
the accuracy of the system depends on the
calibration. In the presence of disturbance, an open-
loop control system will not confirm the desired
task. Open-loop control can be used in practice
only if the relationship between the input and
output is known and if there are neither internal nor
external disturbances. Clearly such systems are not
feedback control systems. Any control system,
which operates on a time basis, is open loop.
Advantages of open loop control systems are
simple construction, very much convenient when
output is difficult to measure, and such systems are
easy from maintenance point of view. Generally,
these are free from the problems of stability. Such
systems are simple to design, economical and give
inaccurate results if there are variations in the
external environment i.e. they cannot sense
environmental changes. They are inaccurate and
unreliable because accuracy of such system is
totally dependent on the accurate pre-calibration of
the controller. Similarly they cannot sense internal
disturbances in the system, after the controller stage.
To maintain the quality and accuracy, recalibration
of the controller is necessary from time-to-time. To
overcome all the above disadvantages, generally
closed loop systems are used in practice.
Closed loop control system is one in
which the output signal has a direct effect upon the
control action. That is, a closed-loop control system
incorporates feedback element. The actuating error
signal, which is the difference between the input
signal and the feedback signal (which may be the
output signal or function of the output signal and its
derivatives), is fed to the controller so as to reduce
the error. In other words, the term „closed loop‟
implies the use of feedback action in order to
reduce the system error. The error signal produced
in the automatic controller is amplified and the
output of the controller is sent to the control value
in order to change the valve opening for steam
supply so as to correct the actual water temperature.
If there is no error, no change in the valve operation
is necessary. The control of a complex system by a
human operation is not effective because of the
many interrelations among various variables.
Automatic control systems eliminate any human
error in operation. If high precision control is
necessary, control must be automatic. System in
which the controlling action or input is somehow

dependent on the output or changes in output are
called closed loop system. Feedback is a property
of the system by which it permits the output to be
compared with the reference input so that
appropriate controlling action can be decided. In
such a system, output or part of the output fed back
to the input for comparison with the reference input
applied to it. It is not possible in all the systems that
available signal can be applied as input to the
system. Depending upon the nature of controller it
is required to reduce it or amplify to change its
nature. This changed input as per requirement is
called reference input, which is to be generated by
using reference transducer. The main excitation to
make the system called its command input, which
is then applied to the reference transducer to
generate reference input. The output, which is to be
decided by feedback element, is fed back to the
reference input. The signal, which is output of
feedback element, is called „feedback signal‟ b(t). It
is then compared with the reference input giving
error signal e(t)=r(t)+b(t). When feedback signal is
positive it is called positive feedback system and if
the signal is negative it is called negative feedback
system. This modified error signal then actuates the
actual system and produces the controlled output
c(t).
An advantage of closed loop control
system is that the use of feedback makes the system
response relatively insensitive to external
disturbances and internal variations in the system
parameters. It is thus possible to use relatively
inaccurate and inexpensive components to obtain
the accurate control of a given plant, whereas this is
impossible in the open-loop case. From the point of
ability, the open-loop control system is easier to
build since stability is not a major problem. On the
other hand, stability is always a major problem in
the closed-loop control system since it may tend to
over correct errors, which may cause oscillations of
constant or changing amplitude. It should be
emphasized that for systems in which the inputs are
known ahead of time and in which there are no
disturbances, it is advisable to use open-loop
control. Closed-loop control systems have
advantages only when unpredictable disturbances
and/or unpredictable variations in system
components are present. A proper combination of
open-loop and closed-loop control is usually less
expensive and satisfies the overall system
performance. The design and implementation of
smart structural systems necessitates the integration
of mechanical systems with sensors, actuators, and
control systems for higher performance and self-
diagnosis capabilities. A key element of this
combination is the integration of the control system
into the structure [4]. The process control system is
the entity that is charged with the responsibility for
monitoring outputs, making decisions about how
best to manipulate inputs so as to obtain desired
output behavior, and effectively implement such
decisions on the process [5]. The process has a
property called self-regulation. A self- regulating
system does not provide regulation of a variable to
any particular reference value. In process control,
the basic objective is to regulate the value of some
quantity. To regulate means to maintain that
quantity at some desired value regardless of
external influences. The desired value is called the
reference value or set point. In many industrial
process control systems, the control process is
complex in mechanism, and varying with time. So,
general PID control is very difficult to obtain
satisfactory effects because it is not self-adaptive
for many varying factors such as parameter varying.
The process dynamics are concerned with
analyzing the dynamic (i.e, time dependent)
behavior of a process in response to various types
of inputs. In other words, it is the behavior of a
process as time progresses [6, 7]. A process is a
progressively continuing operation that concedes of
a series of controlled actions or movements
systematically directed towards a particular result
or end. When the automatic control is applied to
system, which is designed to regulate the value of
some variable to a set point, it is called process
control. Examples are chemical, economic, and
biological processes. An automatic regulation
system in which the output is a variable such as
temperature, pressure, flow, liquid level, or pH is
called a process control system. Process control is
widely applied in industry. Programmed control
such as the temperature control of heating furnaces
in which the furnace temperature is controlled
according to preset program is often used in such
systems. For example, a preset program may be
such that the furnace temperature is raised to a
given temperature in some given time interval and
then lower to another given temperature in some
other given time interval. In such program control,
the set point is varied according to the present time
schedule. The controller then functions to maintain
the furnace temperature close to the varying set
point. It should be noted that most process control
systems include servomechanism as an integral part
[8].Process engineers are often responsible for the
operation of chemical processes. As these processes
become larger scale and/or more complex, the role
of process automation becomes more and more
important. The objective of this textbook is to teach
process engineers how to design and tune feedback
controllers for the automated operation of chemical
processes.

II. LITERATURE REVIEW
This section covers the research findings
related to tuning of industrial processes through
different tuning algorithm. There are number of
control strategies and methods for controlling
concentration and temperature of process model
which have been implemented by researchers from
all over the world. The formulation of variety
objective of overcoming the nonlinearity problem
by having a robust and effective controller as
compared to the conventional techniques.
A.Jayachitra [1] Genetic algorithm (GA)
based PID (proportional integral derivative)
controller has been proposed for tuning optimized
PID parameters in a continuous stirred tank reactor
(CSTR) process using a weighted combination of
objective functions, namely, integral square error
(ISE), integral absolute error (IAE), and integrated
time absolute error (ITAE). Optimization of PID
controller parameters is the key goal in chemical
and biochemical industries. PID controllers have
narrowed down the operating range of processes
with dynamic nonlinearity. In this work, globally
optimized PID parameters tend to operate the
CSTR process in its entire operating range to over
come the limitations of the linear PID controller.
The simulation study reveals that the GA based PID
controller tuned with fixed PID parameters
provides satisfactory performance in terms of set
point tracking and disturbance rejection..
Wen Tan [2]the author compared the
well-known PID tuning rules. Criteria based on
disturbance rejection and system robustness are
proposed to assess the performance of PID
controllers. A simple robustness measure is defined
and the integral gains of the PID controllers are
shown to be a good measure for disturbance
rejection. The integral error is generally accepted as
a good measure for system performance. Clearly, if
the response is critically damped, IE will be equal
to IAE. However, if it is weakly damped, then IE
will not be suitable as a performance measure.
K. J. Åström et al. [3] analyzed the
Ziegler-Nichols step response method for PID
control. The Ziegler-Nichols step response method
is based on the idea of tuning controllers based on
simple features of the step response. This paper has
revisited tuning of PID controllers based on step
response experiments in the spirit of Ziegler and
Nichols. Ziegler and Nichols developed their tuning
rules by simulating a large number of different
processes, and correlating the controller parameters
with features of the step response. Process
dynamics was characterized by the parameters
obtained from the step response. A nice feature of
this design method is that it permits a clear trade-
off between robustness and performance. The idea
is investigated from the point of view of robust
loop shaping. The results are insight into the
properties of PI and PID control and simple tuning
rules that give robust performance for processes
with essentially monotone step responses.
Geetha, M., Manikandan [4] the author
presents the optimal design of PID controller based
on a particle swarm optimization (PSO) approach
for continuous stirred tank reactor (CSTR). The
mathematical model of experimental system had
been approximate near the operating point for the
PSO algorithm to adjust PID parameters for the
minimum integral of time multiplied by absolute
error (ITAE) condition. This research explains a
design of PID controller by using the PSO method
to search for optimal parameters converting into the
optimal point and the good control response based
on the optimal values by the PSO technique.
S. Palanki et al. [5] developed software
module to run a simulation via the internet. The
software module is developed in MATLAB and
simulates a regulation problem in a continuous
stirred tank reactor (CSTR) in which a series
reaction is occurring. The user has the option to
input a wide variety of system parameters, initial
conditions, final time, and controller parameters.
The effect of changing these values on the overall
system dynamics can be studied easily. The
development of such modules eliminates space,
time, and cost constraints. It was found that this
software module was a useful teaching supplement
to the traditional classroom lecture. Students were
able to study the effect of changing various process
parameters as well as controller parameters on the
regular output. The interactive distance learning
concepts include the use of remote computer access
to enhance self-paced learning. The internet
provides a real-time link that eliminates space time
constraints, and gives access from anywhere at any
time. Moreover, due to the multiuser-multitasking
nature of computer environments, several students
can run the software module at the same time. The
development of a virtual laboratory has the
potential to deliver experiences which are not
accessible to students in the real world. Recent
technological advances in computer software are
bringing virtual laboratories within the reach of
educational and student budgets.
J. Kennedy et al. [6] introduced particle
swarm methodology for the optimization of non-
linear functions. Particle swarm optimization is an
extremely simple algorithm that seems to be
effective for optimizing a wide range of functions.
We view it as a biologically derived algorithm,
occupying the space in nature between evolutionary
searches, which occurs on the order of milliseconds.
Particle swarm optimization as developed by
authors comprises a very simple concept, and
paradigms can be implemented in a few lines of

computer code. It requires only primitive
mathematical operators, and is computationally
inexpensive in terms of both memory requirements
and speed. Early testing has found the
implementation to be effective with several kind of
problems. This paper discusses application of the
algorithm to the training of artificial neural network
weights. Particle swarm optimization has also been
demonstrated to perform well on genetic algorithm
test functions. The adjustment toward pbest and
gbest by the particle swarm optimizer is
conceptually similar to the crossover operation
utilized by genetic algorithms.
B. Nagaraj et al. [7] compared the
different soft computing techniques for PID
controller. The methodology and efficiency of the
proposed method are compared with that of
traditional methods. Determination or tuning of the
PID parameters continues to be important as these
parameters have a great influence on the stability
and performance of the control system. Research
work has been carried out to get an optimal PID
tuning by using GA, EP, PSO and ACO. The
results obtained reflect that use of soft computing
based controller improves the performance of
process in terms of time domain specifications, set
point tracking, and regulatory changes and also
provides and optimum stability.
Parvesh Saini et al. [8] The Continuous
stirred tank reactor is most deciding section
chemical processes based industries. It is necessary
to have to have the proper analysis of CSTR in
chemical industries for obtaining the desire product.
The two controllers are present in this paper (Fuzzy
logic Controller (FLC), Proportional-Integral-
Derivative(PID) are designed to control the CSTR
temperature and a comparative analysis of the
controllers have been presented. In the presence of
step disturbance the behavior of CSTR has been
tested them and then the control methodologies
have been developed to control the temperature. In
the chemical process insutries the CSTR shows the
extremely non-linear behavior and generally has
varied operating range.
Bikas Mondal et al. [9] in many industrial
applications conventional controller are used due to
their simplicity and robustness. All the systems are
non-linear in nature: Conventional controller is not
always providing good and correct result. So for
better response Fuzzy Logic Controller are used.
IN this paper to compare the control effect due to
PID control and Fuzzy control. When comparing
the fuzzy control and PID control will see that
fuzzy control is superior then the PID control. More
concentration is provided by fuzzy control to
different parameters, such as the response time, the
steady state error and overshoot. The paper work
indicates that in triangular membership function
and centroid defuzzification method the fuzzy logic
controller application gives the best response. The
PID controller are easy to implement and also easy
to understand in hardware and software, and very
simple and a process model is not required for
operation and initialization. Conventional PID is
not very efficient due to the presence of non-
linearity. In various industrial processes Fuzzy
Logic Controller (FLC) are used for taking proper
action likes human control actions.
G. Reynoso-Meza et al. [10] For the
several industrial processes Proportional - Integral -
Derivative (PID) controllers remains as reliable and
practical control solutions. The major merit of the
PID controller is they are easy to implement and
gives a better trade-off between simplicity and cost
of implement. In this paper for unstable process
propose a multi-objective optimization
design ,using PID controller. The procedure for this
kind of process, sometimes difficult to control;
comparison with existing tuning rule methods
provide promising results for this tuning procedure.
For the fulfilment of the several objectives and
requirements new techniques are being focused,
sometimes in conflict among them. For controller
tuning applications recently a new technique is
have been shown that is Multiobjective
Optimization (MOO). The Multiobjective
Optimization technique unable the decision maker
and designer having having a close embedment into
the tuning process since it is possible to take into
account each design objective individually; the
comparing design alternatives are also enable by
this technique(that is different controllers), in order
to select a tuning fulfilling the expected trade-off
among conflicting objectives. In the open loop
stable systems efforts are particularly concentrated;
nevertheless some critical processes as continuous
stirred tank reactors and bioreactors, common in
chemical processing units and biological processes,
are unstable open loop systems. For such process;
nevertheless, efforts to merge multi-objective
optimization techniques have been not yet applied
for such instances PID- like controller tuning have
been main focus in work.
J.C. Basilio et al. [11] proposed
methodologies for tuning PI and PID controllers.
Like the well-known Ziegler- Nichols method, they
are based on the plant step response. The
methodology also encompasses the design of PID
controllers for plants with under damped step
response and provides the means for a systematic
adjustment of the controller gain in order to meet
transient performance specifications. Unlike the
Ziegler-Nichols step response method, they provide
systematic means to adjust the proportional gain in
order to have no overshoot on the closed-loop step
response. In addition, since all the development of

the methodology relies solely on concepts
introduced in a frequency-domain-based control
course, the paper has also a didactic contribution
III. CONCLUSIONS
The proposed PSO-PID and HU-PID
controller is tested by using Mat-lab Simulink
program and their performance is compared. A HU-
PID controller is best implemented. Also, the PID
controller parameters obtained from HU algorithm
gives better tuning result as compared to PSO-PID
rule. The major impact of HU is on integral square
error and peak overshooting. Both are minimized
by HU-PID controller. HU-PID is a very simple
concept, and paradigms can be implemented in a
few lines of computer code.
REFER
ENCES
[1]. Jayachitra, A., and R. Vinodha. "Genetic
algorithm based PID controller tuning
approach for continuous stirred tank
reactor." Advances in Artificial Intelligence,
vol 12, 2014 (2014):9.
[2]. Zribi, Ali, Mohamed Chtourou, and
Mohamed Djemel. "A New PID Neural
Network Controller Design for Nonlinear
Processes." arXiv preprint
arXiv:1512.07529 , vol 5, (2015).
[3]. Mohanty, Asit, Meera Viswavandya, and
Sthitapragyan Mohanty. "An optimised
FOPID controller for dynamic voltage
stability and reactive power management in
a stand-alone micro grid." International
Journal of Electrical Power & Energy
Systems vol 78 (2016): 524-536.
[4]. Geetha, M., Manikandan, P., Shanmugapriya,
P., Silambarasan, V., & Naveen, R. (2013,
July). Real-time implementation and
performance analysis of two dimension PID
fuzzy controller for continuous stirred tank
reactor. In Computing, Communications and
Networking Technologies (ICCCNT), 2013
Fourth International Conference on (pp. 1-5,
vol 2). IEEE.
[5]. Perng, Jau-Woei, Guan-Yan Chen, and Ya-
Wen Hsu. "FOPID controller optimization
based on SIWPSO-RBFNN
algorithm for fractional-order time delay
systems." Soft Computing (2016): 1-14.
[6]. K. J. Astrom and T. Hagglund, “Revisiting
the Ziegler-Nichols step response method for
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[7]. Smith C.A, and Corripio, A.B, “Principles
and Practice of Automatic Process Control:,
John Wiley and Sons, 1985.Kastauhiko
Ogata, “Modern control engineering”, PIII,
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[9]. Wen Tan, Jizhen Liu, Tongwen Chen and
Horaciao J. Marquez, “Comparision of some
well known PID tuning formulas,” Trans.
on Computers and Chemical Engineering,
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[10]. K. J. Åström and T. Hägglund, “Revisiting
the Ziegler-Nichols step response method for
PID control,” Journal of Process control, vol.
14, pp. 635-650,2004.
[11]. Mohammad Ali Nekoui, Mohammad Ali
Khameneh and Mohammad Hosein
Kazemi,“Optimal design of PID controller
for a CSTR system using Particle swarm
optimization,” IEEE International Power
Electronics and Motion Control Conference,
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[12]. S. Palanki and S. Kolavennu, “ Simulation of
control of a CSTR Process,” International
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pp.398-402,2003.
[13]. J. Kennedy and R. Eberhart, “Particle
swarm Optimization” Proc. IEEE
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Networks, vol.4, pp. 1942-1948,1995.
[14]. B. Nagaraj and P. Vijayakumar, “A
comparative study of PID controller tuning
using GA, EP, PSO, and ACO,” Journal of
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